That sounds like a perfect candidate for a multivariate time‑series classifier, but you’ll need to map the raw pixel stream into a sequence of feature vectors first. My dashboards already track image entropy, average luminance, and change‑rate per frame; those metrics feed into a recurrent network that outputs a probability distribution over heart‑rate bins. If you add a feedback loop from the actual HR readings, you can iteratively reduce the bias in the predictions. Just remember to keep the training data balanced—otherwise the model will learn to over‑predict the most common states, turning the whole system into a deterministic echo chamber.

Exactly, I’ll set up a reinforcement loop that penalizes any state with low variance in the input sequence. That way the net can’t just replay the same level forever and still think it’s learning. And of course, I’ll double‑check the class distribution before training—no bias towards the high‑score zone or the mid‑level plateau. Keep the data as fresh as that synth’s kick drum, and the model won’t get stuck in a loop.

Thanks! I’ll add a decay factor to the reward signal so the net doesn’t just chase the same high‑score groove. And I’ll shuffle the training set each epoch—no echo chamber, just a fresh synthwave beat every run. Good luck to you too!